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Small updates

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Per.Andreas.Brodtkorb 14 years ago
parent 69dd04d787
commit 767cae0ed8
2 changed files with 138 additions and 13 deletions
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150
pywafo/src/wafo/kdetools.py
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@ -10,10 +10,12 @@
#-------------------------------------------------------------------------------
#!/usr/bin/env python
from __future__ import division
import copy
import warnings
import numpy as np
from numpy import pi, sqrt, atleast_2d, exp, newaxis, array #@UnresolvedImport
import scipy
from scipy import interpolate
from scipy import linalg
from scipy.special import gamma
from misc import tranproc, trangood
@ -111,7 +113,15 @@ class TKDE(object):
>>> f
array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318,
0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882])
>>> kde.eval_grid(x)
array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318,
0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882])
>>> kde.eval_grid_fast(x)
array([ 0. , 1.16200356, 0.99256178, 0.81930973, 0.65479862,
0.51021576, 0.3896221 , 0.29266142, 0. , 0. ])
import pylab as plb
h1 = plb.plot(x, f) # 1D probability density plot
t = np.trapz(f, x)
@ -125,11 +135,13 @@ class TKDE(object):
self.alpha = alpha
self.xmin = xmin
self.xmax = xmax
self.inc = inc
self.L2 = L2
self.d, self.n = self.dataset.shape
self.initialize()
def initialize(self):
self._set_xlimits()
tdataset = self._dat2gaus(self.dataset)
xmin = self.xmin
if xmin is not None:
@ -137,9 +149,27 @@ class TKDE(object):
xmax = self.xmax
if xmax is not None:
xmax = self._dat2gaus(xmax)
self.kde = KDE(tdataset, self.hs, self.kernel, self.alpha, xmin, xmax,
self.tkde = KDE(tdataset, self.hs, self.kernel, self.alpha, xmin, xmax,
self.inc)
def _set_xlimits(self):
amin = self.dataset.min(axis=-1)
amax = self.dataset.max(axis=-1)
xyzrange = amax-amin
offset = xyzrange/4.0
if self.xmin is None:
self.xmin = amin - offset
else:
self.xmin = self.xmin * np.ones(self.d)
if self.xmax is None:
self.xmax = amax + offset
else:
self.xmax = self.xmax * np.ones(self.d)
if self.L2 is not None:
L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation
self.xmin = np.where(L2!=1, np.maximum(self.xmin, amin/2.0), self.xmin)
def _check_shape(self, points):
points = atleast_2d(points)
d, m = points.shape
@ -160,11 +190,22 @@ class TKDE(object):
L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation
tpoints = points.copy()
tpoints = copy.copy(points)
for i, v2 in enumerate(L2.tolist()):
tpoints[i] = np.where(v2 == 0, np.log(points[i]), points[i] ** v2)
tpoints[i] = np.log(points[i]) if v2 == 0 else points[i] ** v2
return tpoints
def _gaus2dat(self, tpoints):
if self.L2 is None:
return tpoints # default no transformation
L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation
points = copy.copy(tpoints)
for i, v2 in enumerate(L2.tolist()):
points[i] = np.exp(tpoints[i]) if v2 == 0 else tpoints[i] ** (1.0/v2)
return points
def _scale_pdf(self, pdf, points):
if self.L2 is None:
return pdf
@ -177,7 +218,70 @@ class TKDE(object):
transformation. Check the KDE for spurious spikes'''
warnings.warn(msg)
return pdf
def eval_grid_fast(self, *args):
"""Evaluate the estimated pdf on a grid.
Parameters
----------
arg_0,arg_1,... arg_d-1 : vectors
Alternatively, if no vectors is passed in then
arg_i = linspace(self.xmin[i], self.xmax[i], self.inc)
Returns
-------
values : array-like
The values evaluated at meshgrid(*args).
"""
if self.L2 is None:
f = self.tkde.eval_grid_fast(*args)
self.args = self.tkde.args
return f
#targs = self._dat2gaus(list(args)) if len(args) else args
tf = self.tkde.eval_grid_fast()
self.args = self._gaus2dat(list(self.tkde.args))
points = meshgrid(*self.args) if self.d>1 else self.args
f = self._scale_pdf(tf, points)
if len(args):
if self.d==1:
pdf = interpolate.interp1d(points[0],f, bounds_error=False, fill_value=0.0)
elif self.d==2:
pdf = interpolate.interp2d(points[0],points[1], f, bounds_error=False, fill_value=0.0)
ipoints = meshgrid(*args) if self.d>1 else args
fi = pdf(*ipoints)
fi.shape = ipoints[0].shape
return fi
return f
def eval_grid(self, *args):
"""Evaluate the estimated pdf on a grid.
Parameters
----------
arg_0,arg_1,... arg_d-1 : vectors
Alternatively, if no vectors is passed in then
arg_i = linspace(self.xmin[i], self.xmax[i], self.inc)
Returns
-------
values : array-like
The values evaluated at meshgrid(*args).
"""
if len(args)==0:
args = []
for i in range(self.d):
args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc))
self.args = args
if self.L2 is None:
return self.tkde.eval_grid(*args)
targs = self._dat2gaus(list(args))
tf = self.tkde.eval_grid(*targs)
points = meshgrid(*args) if self.d>1 else self.args
f = self._scale_pdf(tf, points)
return f
return self.tkde.eval_grid(*args)
def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
@ -198,10 +302,10 @@ class TKDE(object):
the dimensionality of the KDE.
"""
if self.L2 is None:
return self.kde(points)
return self.tkde(points)
points = self._check_shape(points)
tpoints = self._dat2gaus(points)
tf = self.kde(tpoints)
tf = self.tkde(tpoints)
f = self._scale_pdf(tf, points)
return f
@ -258,6 +362,24 @@ class KDE(object):
array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
>>> kde.eval_grid(x)
array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
>>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0)
>>> kde0.evaluate(x)
array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 ,
0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821])
>>> kde0.eval_grid(x)
array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 ,
0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821])
>>> f = kde0.eval_grid_fast()
>>> np.interp(x, kde0.args[0], f)
array([ 0.54344 , 1.04793706, 1.38047458, 1.28324876, 0.943131 ,
0.63570524, 0.39404219, 0.19450807, 0.08505344, 0.08505344])
import pylab as plb
h1 = plb.plot(x, f) # 1D probability density plot
t = np.trapz(f, x)
@ -345,8 +467,10 @@ class KDE(object):
"""
if len(args)==0:
args = []
for i in range(self.d):
args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc))
self.args = args
return self._eval_grid_fast(*args)
def _eval_grid_fast(self, *args):
X = np.vstack(args)
@ -360,16 +484,16 @@ class KDE(object):
for i in range(d):
Xn.append(x0*dx[i])
Xnc = meshgrid(*Xn)
Xnc = meshgrid(*Xn) if d>1 else Xn
shape0 = Xnc[0].shape
for i in range(d):
Xnc[i].shape = (-1,)
Xn = np.dot(np.vstack(Xnc), self.inv_hs)
Xn = np.dot(self.inv_hs, np.vstack(Xnc))
# Obtain the kernel weights.
kw = self.kernel(Xn)
kw = self.kernel(Xn)/self._norm_factor
kw.shape = shape0
kw = np.fft.ifftshift(kw)
fftn = np.fft.fftn
@ -401,13 +525,15 @@ class KDE(object):
"""
if len(args)==0:
args = []
for i in range(self.d):
args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc))
self.args = args
return self._eval_grid(*args)
def _eval_grid(self, *args):
grd = meshgrid(*args)
grd = meshgrid(*args) if len(args)>1 else list(args)
shape0 = grd[0].shape
d = len(grd)
for i in range(d):

1
pywafo/src/wafo/test/test_kdetools.py
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@ -166,7 +166,6 @@ def test_gridcount_2D():
[ 0.02063536, 0.31054405, 0.71865964, 0.13486633, 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
h = plb.plot(x, c, '.') # 1D histogram
h1 = plb.plot(x, c / dx / N) # 1D probability density plot

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